{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:R6IWZIIODZ4MB3P4D2ON4VDCYY","short_pith_number":"pith:R6IWZIIO","schema_version":"1.0","canonical_sha256":"8f916ca10e1e78c0edfc1e9cde5462c60a128e650ff08f6b4aaa824e86a74ec6","source":{"kind":"arxiv","id":"2505.00917","version":1},"attestation_state":"computed","paper":{"title":"Multivariate Conformal Selection","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.LG","stat.ML"],"primary_cat":"stat.ME","authors_text":"Archer Y. Yang, Tian Bai, Xiang Yu, Yue Zhao","submitted_at":"2025-05-01T23:33:57Z","abstract_excerpt":"Selecting high-quality candidates from large datasets is critical in applications such as drug discovery, precision medicine, and alignment of large language models (LLMs). While Conformal Selection (CS) provides rigorous uncertainty quantification, it is limited to univariate responses and scalar criteria. To address this issue, we propose Multivariate Conformal Selection (mCS), a generalization of CS designed for multivariate response settings. Our method introduces regional monotonicity and employs multivariate nonconformity scores to construct conformal p-values, enabling finite-sample Fal"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2505.00917","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ME","submitted_at":"2025-05-01T23:33:57Z","cross_cats_sorted":["cs.AI","cs.LG","stat.ML"],"title_canon_sha256":"5790f65159d60dcbed1b113ace80c016dbfac23a22b1f675afa2e866074c28e5","abstract_canon_sha256":"3742689d53c9889f97f9a9fc6166732ebddbc92cffa22627452484a3c445dcba"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:57:41.788749Z","signature_b64":"C5avxCAgmJr0i6z/t0FGq56oCKKs2ozm/fA0GO5Ik8zf4g5exZfJitHUJOHDWiQPMXQR6FB6zV3EzXyCSJ7MAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8f916ca10e1e78c0edfc1e9cde5462c60a128e650ff08f6b4aaa824e86a74ec6","last_reissued_at":"2026-07-05T10:57:41.788115Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:57:41.788115Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Multivariate Conformal Selection","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.LG","stat.ML"],"primary_cat":"stat.ME","authors_text":"Archer Y. Yang, Tian Bai, Xiang Yu, Yue Zhao","submitted_at":"2025-05-01T23:33:57Z","abstract_excerpt":"Selecting high-quality candidates from large datasets is critical in applications such as drug discovery, precision medicine, and alignment of large language models (LLMs). While Conformal Selection (CS) provides rigorous uncertainty quantification, it is limited to univariate responses and scalar criteria. To address this issue, we propose Multivariate Conformal Selection (mCS), a generalization of CS designed for multivariate response settings. Our method introduces regional monotonicity and employs multivariate nonconformity scores to construct conformal p-values, enabling finite-sample Fal"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.00917","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2505.00917/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2505.00917","created_at":"2026-07-05T10:57:41.788187+00:00"},{"alias_kind":"arxiv_version","alias_value":"2505.00917v1","created_at":"2026-07-05T10:57:41.788187+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.00917","created_at":"2026-07-05T10:57:41.788187+00:00"},{"alias_kind":"pith_short_12","alias_value":"R6IWZIIODZ4M","created_at":"2026-07-05T10:57:41.788187+00:00"},{"alias_kind":"pith_short_16","alias_value":"R6IWZIIODZ4MB3P4","created_at":"2026-07-05T10:57:41.788187+00:00"},{"alias_kind":"pith_short_8","alias_value":"R6IWZIIO","created_at":"2026-07-05T10:57:41.788187+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/R6IWZIIODZ4MB3P4D2ON4VDCYY","json":"https://pith.science/pith/R6IWZIIODZ4MB3P4D2ON4VDCYY.json","graph_json":"https://pith.science/api/pith-number/R6IWZIIODZ4MB3P4D2ON4VDCYY/graph.json","events_json":"https://pith.science/api/pith-number/R6IWZIIODZ4MB3P4D2ON4VDCYY/events.json","paper":"https://pith.science/paper/R6IWZIIO"},"agent_actions":{"view_html":"https://pith.science/pith/R6IWZIIODZ4MB3P4D2ON4VDCYY","download_json":"https://pith.science/pith/R6IWZIIODZ4MB3P4D2ON4VDCYY.json","view_paper":"https://pith.science/paper/R6IWZIIO","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2505.00917&json=true","fetch_graph":"https://pith.science/api/pith-number/R6IWZIIODZ4MB3P4D2ON4VDCYY/graph.json","fetch_events":"https://pith.science/api/pith-number/R6IWZIIODZ4MB3P4D2ON4VDCYY/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/R6IWZIIODZ4MB3P4D2ON4VDCYY/action/timestamp_anchor","attest_storage":"https://pith.science/pith/R6IWZIIODZ4MB3P4D2ON4VDCYY/action/storage_attestation","attest_author":"https://pith.science/pith/R6IWZIIODZ4MB3P4D2ON4VDCYY/action/author_attestation","sign_citation":"https://pith.science/pith/R6IWZIIODZ4MB3P4D2ON4VDCYY/action/citation_signature","submit_replication":"https://pith.science/pith/R6IWZIIODZ4MB3P4D2ON4VDCYY/action/replication_record"}},"created_at":"2026-07-05T10:57:41.788187+00:00","updated_at":"2026-07-05T10:57:41.788187+00:00"}